CN114238573A - Information pushing method and device based on text countermeasure sample - Google Patents
Information pushing method and device based on text countermeasure sample Download PDFInfo
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Abstract
The application discloses an information pushing method and device based on a text counterexample, relates to the technical field of artificial intelligence, and mainly aims to solve the technical problem that the information pushing effectiveness is low due to the fact that the text counterexample cannot be effectively generated in the prior art. The method comprises the following steps: acquiring evaluation text information of a target object; classifying the evaluation text information based on the text emotion analysis model after model training is completed to obtain emotion classification results; if the emotion classification result is positive emotion, extracting the management type and the key words of the target object, and searching for the associated object with the similarity larger than a preset similarity threshold value with the key words in a preset classification management database under the same management type with the target object; and if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result. The method is mainly used for pushing information.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to an information push method and apparatus based on a text countermeasure sample.
Background
With the rapid development of artificial intelligence technology, most intelligent medical systems introduce text intelligent management systems. Generally, a large amount of text label data can be collected in advance, a model is trained by using a machine learning or deep learning method, and then information matched with the interest of the user is recommended. The precision of the model directly influences the experience of the user in information pushing aiming at the text, so that big data is the premise of machine learning, and the precision of the model can be effectively improved only by a large amount of labeled data. However, in reality, only a small amount of text label data is often available, and the cost of manual labeling is very high, so that a method of generating counterexample based on original data is mostly adopted at present.
However, most of the evaluations made by the user for different medical products and items are text data, and the generation of text countermeasures is difficult for the natural language processing field, and the main reason is that for the image processing field, the image data belongs to continuous data, and after being processed by means of rotation, cutting and the like, the characteristics of the original data can still be maintained, but the text data belongs to discrete data, and after being processed by means of traditional replacement, deletion and the like, the context coherence of the text and the semantic situation expressed by the text may be changed. Therefore, the effective generation of the text countermeasure sample becomes one of the hot tasks of the recommendation module in the intelligent management system.
Disclosure of Invention
In view of the above, the present application provides an information pushing method and apparatus based on a text counterexample, and mainly aims to solve the technical problem of low information pushing effectiveness caused by the failure to effectively generate a text counterexample in the prior art.
According to one aspect of the application, an information pushing method based on a text countermeasure sample is provided, and the method comprises the following steps:
acquiring evaluation text information of a target object;
classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training samples which are obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model;
if the emotion classification result is positive emotion, extracting the management type and the key words of the target object, and searching for the associated object with the similarity larger than a preset similarity threshold value with the key words in a preset classification management database under the same management type with the target object;
and if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result.
Preferably, before the classification processing is performed on the evaluation text information based on the text emotion analysis model after model training is completed, the method further includes:
acquiring full-scale initial evaluation text information to obtain an emotion analysis training original sample set;
an initial text emotion analysis model is established based on a convolutional neural network, model training is carried out on the initial text emotion analysis model based on an emotion analysis training original sample set, and a basic text emotion analysis model is obtained.
Preferably, after obtaining the base text emotion analysis model, the method further includes:
calculating an importance parameter of a marked word in the evaluation text information based on the basic text sentiment analysis model, selecting the marked word corresponding to the maximum value of the importance parameter as a seed word of the evaluation text information, wherein the evaluation text information is evaluation text information in the sentiment analysis training original sample set;
predicting corresponding marked words of the seed word positions based on a pre-training language representation model, acquiring a preset number of marked words to replace the seed words, and generating a confrontation sample to be measured of the evaluation text information;
and measuring a similarity parameter between the confrontation sample to be measured and the evaluation text information based on a semantic similarity model, selecting the confrontation sample to be measured corresponding to the maximum value of the similarity parameter as the confrontation sample of the evaluation text information, and generating an emotion analysis training extended sample set.
Preferably, after generating the emotion analysis training extended sample set, the method further includes:
and training the basic text emotion analysis model based on the training samples obtained by combining the emotion analysis training original sample set and the emotion analysis training extended sample set to obtain a text emotion analysis model.
Preferably, the obtaining of the full initial evaluation text information to obtain an emotion analysis training original sample set specifically includes:
acquiring full-scale initial evaluation text information;
and screening target character strings for the initial evaluation text information to obtain an emotion analysis training original sample set.
Preferably, the method further comprises:
if the emotion classification result is negative emotion, outputting an inquiry box, wherein the inquiry box is used for inquiring whether all objects under the management category of the target object are shielded;
and if the object is shielded, matching a replacement object from the classification management database based on user characteristic information, and pushing the replacement object, wherein the user characteristic information comprises the age, the sex and the borrowing record of the user.
Preferably, the method further comprises:
and if the associated objects do not exist, outputting an attention ranking list, wherein the attention ranking list is used for representing the attention degrees of all the objects in the classification management database.
According to another aspect of the present application, there is provided an information pushing apparatus for a text-based countermeasure example, including:
the first acquisition module is used for acquiring evaluation text information of a target object;
the classification module is used for classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training samples which are obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model;
the searching module is used for extracting the management category and the key word of the target object if the emotion classification result is positive emotion, and searching for the associated object of which the similarity with the key word is greater than a preset similarity threshold under the same management category as the target object in a preset classification management database;
and the first output module is used for sorting based on the similarity value if the associated object exists and outputting the associated object according to a sorting result.
Preferably, before the classification module, the apparatus further includes:
the second acquisition module is used for acquiring the full-scale initial evaluation text information to obtain an emotion analysis training original sample set;
and the construction module is used for constructing an initial text emotion analysis model based on the convolutional neural network, and performing model training on the initial text emotion analysis model based on the emotion analysis training original sample set to obtain a basic text emotion analysis model.
Preferably, after the building of the module, the apparatus further comprises:
the calculation module is used for calculating the importance parameters of the marked words in the evaluation text information based on the basic text sentiment analysis model, selecting the marked words corresponding to the maximum value of the importance parameters as seed words of the evaluation text information, and the evaluation text information is evaluation text information in the sentiment analysis training original sample set;
the prediction module is used for predicting corresponding marked words of the seed word positions based on a pre-training language representation model, acquiring a preset number of marked words to replace the seed words, and generating a confrontation sample to be measured of the evaluation text information;
and the measuring module is used for measuring the similarity parameter between the confrontation sample to be measured and the evaluation text information based on the semantic similarity model, selecting the confrontation sample to be measured corresponding to the maximum value of the similarity parameter as the confrontation sample of the evaluation text information, and generating an emotion analysis training extended sample set.
Preferably, after the measurement module, the apparatus further comprises:
and the training module is used for training the basic text emotion analysis model based on a training sample obtained by combining the emotion analysis training original sample set and the emotion analysis training extended sample set to obtain a text emotion analysis model.
Preferably, the second obtaining module specifically includes:
the acquisition unit is used for acquiring full-scale initial evaluation text information;
and the screening unit is used for screening the target character strings of the initial evaluation text information to obtain an emotion analysis training original sample set.
Preferably, the apparatus further comprises:
a second output module, configured to output an inquiry box if the emotion classification result is a negative emotion, where the inquiry box is used to inquire whether to mask all objects in the management category of the target object;
and the matching module is used for matching a replacement object from the classification management database based on user characteristic information and pushing the replacement object if the replacement object is shielded, wherein the user characteristic information comprises the age, the sex and the borrowing record of the user.
Preferably, the apparatus further comprises:
and the third output module is used for outputting a focus ranking list if the associated object does not exist, wherein the focus ranking list is used for representing the focused degrees of all the objects in the classification management database.
According to another aspect of the present application, a storage medium is provided, where at least one executable instruction is stored, and the executable instruction causes a processor to perform operations corresponding to the information pushing method based on the text countermeasure sample.
According to yet another aspect of the present application, there is provided a computer device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the information pushing method based on the text countermeasure sample.
By means of the technical scheme, the technical scheme provided by the embodiment of the application at least has the following advantages:
the application provides an information pushing method and device based on a text countermeasure sample, and the method comprises the steps of firstly obtaining evaluation text information of a target object; secondly, classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training a training sample which is obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model; if the emotion classification result is positive emotion, extracting the management class and the keywords of the target object, and searching for the associated object with the similarity greater than a preset similarity threshold value with the keywords in a preset classification management database under the same management class with the target object; and finally, if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result. Compared with the prior art, the evaluation text information of the target object is classified by the text emotion analysis model obtained by training the training sample obtained by combining the expanded text information and the original text information, so that the accuracy of classification of the evaluation text information is greatly improved, and the effectiveness of information push according to the classification result is correspondingly improved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of an information pushing method based on a text countermeasure sample according to an embodiment of the present application;
fig. 2 is a flowchart illustrating another information pushing method based on a text countermeasure sample according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a prediction model of a tagged word corresponding to a seed word position according to an embodiment of the present application;
FIG. 4 is a block diagram illustrating an information pushing apparatus based on a text countermeasure sample according to an embodiment of the present application;
fig. 5 shows a schematic structural diagram of a computer device provided in an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Based on this, in an embodiment, as shown in fig. 1, an information pushing method based on a text countermeasure sample is provided, which is described by taking an example that the method is applied to a computer device such as a server, where the server may be an independent server, or a cloud server that provides basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), and big data and an artificial intelligence platform, such as an intelligent medical system, a digital medical platform, and the like. The method comprises the following steps:
101. and acquiring evaluation text information of the target object.
In the embodiment of the application, the execution subject may be an intelligent management system with an information pushing function, for example, an intelligent medical evaluation system, an intelligent library management system, and the like. For example, the current execution subject may be a book intelligent management system with evaluation and push functions, the target object may be a target book evaluated by the current user, and the evaluation text information may be text evaluation information made by the current user on the target graph. When the user completes the purchase of the e-book or returns the paper book, the management system provides the user with an evaluation service about the target book so as to evaluate the content, purchase or lease experience of the target book. The method mainly comprises the steps of performing emotional evaluation on book contents, matching books of the same category according to the interest of a user in the book contents, and further pushing the books to the user. For example, after returning to book B, user a evaluates the text message as "the author sets forth the historical event in an testimony kiss, and cannot see down", and then the emotion based on this evaluated text message is classified as positive emotion, and further, the historical book with the keyword "easy or humorous" is recommended to user a.
It should be noted that the evaluation text information in the embodiment of the present application may be obtained based on a user client of the intelligent book management system, where the intelligent book management system may be an intelligent matching system of a library, so as to facilitate borrowing books by a user; it may also be an electronic book vending platform equipped with rating and push functionality. After the user finishes purchasing the electronic book or returns the book in the paper layout, the user can evaluate the book and transmit the book to the book intelligent management system.
102. And classifying the evaluation text information based on the text emotion analysis model after model training is completed to obtain emotion classification results.
The text emotion analysis model is obtained by training a training sample obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model.
In this embodiment of the application, the text emotion analysis model may be a text emotion analysis model constructed based on a convolutional neural network model, for example, a TextCNN model. And the model training is completed by a training sample obtained by combining a text countermeasure sample (extended text information) generated based on the mask language model and the original text information. Wherein. The text emotion analysis model constructed based on the convolutional neural network model is a text classification model based on the convolutional neural network. The text countermeasure sample is generated based on the mask language model, the original text can be expanded by one time, and then the text emotion analysis model is trained through the training sample consisting of the expanded text formed by the countermeasure sample and the original text, so that the accuracy of the text emotion analysis result can be effectively improved. Due to emotion analysis, the obtained classification result comprises positive emotion and negative emotion, and therefore whether the user is interested in the target object or not can be determined.
103. If the emotion classification result is positive emotion, extracting the management type and the key words of the target object, and searching for the associated object with the similarity greater than a preset similarity threshold value with the key words in the preset classification management database under the same management type with the target object.
In the embodiment of the application, since the emotion classification result represents whether the user is interested in the target object, if the emotion classification result of the user for the evaluation text information of the target object is positive emotion, which indicates that the user likes the target object, the emotion classification result also is interested in the objects of the same category with a high probability. In order to improve the reading experience of the user, the category of the target object and the keywords in the content introduction can be extracted, and the related objects with the similarity larger than the preset similarity threshold value with the keywords are searched under the same management category as the target object in the book classification management database. The library classified management database can be set by an intelligent matching system of a library and is used for classified management of library books so as to be convenient for users to look up; or may be provided by the electronic book vending platform for easy retrieval when vending to the user. For example, the "tomb stealing note series" and the "ghost lamp series" are both stored in the book classification management database under the categories of "suspicion and adventure", the keyword is "tomb stealing", when the emotion classification result of the evaluation text information of the user a on the "tomb stealing note series" book is positive emotion, the categories of "suspicion and hair line" and the keyword "tomb stealing", and the associated book "ghost lamp series" is found.
104. And if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result.
In the embodiment of the present application, if the associated objects associated with the keywords and having the same category as the target object are found, the associated objects are sorted based on the similarity values, the associated objects are output according to the sorting result, the associated objects may be output in a sequence from high to low, or in a sequence from low to high, which may be specifically set by a user, and the embodiment of the present application is not particularly limited.
It should be noted that the target objects have the same category, the associated objects associated with the keywords may be 1 item or multiple items, and the associated objects are output according to the similarity ranking result, so that the user can firstly see the associated objects with high association with the target object interested by the user according to the selection of the user, and can click and view the associated objects more easily.
The embodiment of the application provides another information pushing method based on a text counterexample, as shown in fig. 2, the method includes:
201. acquiring full-scale initial evaluation text information;
in the embodiment of the application, taking an intelligent book management system with evaluation and push functions as an example, before a model is built, first, full initial evaluation text information is obtained. The evaluation text information of all books stored in the intelligent book management system is marked and then used as full initial evaluation text information.
202. And screening target character strings for the initial evaluation text information to obtain an emotion analysis training original sample set.
In the embodiment of the present application, the total initial evaluation text information may be specifically expressed as: g { (x)1,y1),...,(xi,yi),...,(xn,yn) Wherein (x)i,yi) Respectively representing the ith evaluation text information and the corresponding positive and negative face category labels in the data set G, n represents the quantity of the data set, and y represents any evaluation text informationiE {0,1}, 0 denotes a positive label and 1 denotes a negative label. Because the text information of the original book contains a large number of useless characters, the evaluation text information is required to be subjected to data preprocessing, the data preprocessing mainly comprises word segmentation, word stop removal, punctuation removal and other operations, and finally the ith evaluation text information can be represented as
Wherein, wijExpressed as rating text information xiAnd (3) preprocessing the jth word or word (token), wherein l represents the length of the preprocessed text, and the preprocessed data set G can be represented as D.
203. An initial text emotion analysis model is established based on a convolutional neural network, model training is carried out on the initial text emotion analysis model based on an emotion analysis training original sample set, and a basic text emotion analysis model is obtained.
In the embodiment of the present application, based on the evaluation text information D preprocessed in step 202, for example, the embodiment of the present application trains the evaluation text information by using a TextCNN model to generate an initial text emotion analysis model M1. After the model training is completed, the model M1The probability that the evaluation text information belongs to positive and negative emotions can be effectively predicted.
Sample xiProbability P of belonging to a positive emotioniCan be expressed as:
Pi＝M1(xi)
sample xiProbability N of belonging to a negative emotioniCan be expressed as:
Ni＝1-Pi
204. and calculating the importance parameters of the marked words in the evaluation text information based on the basic text emotion analysis model, and selecting the marked words corresponding to the maximum value of the importance parameters as seed words of the evaluation text information.
And the evaluation text information is the evaluation text information in the emotion analysis training original sample set.
In the embodiment of the application, for evaluating text information
Any one of the marked words wijThe importance parameter of (a) is calculated as follows:
from evaluating text information xiDeletion of wijIs recorded as xi[≠j]Then x is addedi[≠j]Input to model M1The score is predicted, and the formula is as follows:
wherein p isi[≠j]Represents xi[≠j]Input to model M1The score of (a), yiRepresents a sample xiTrue mark ofTabs, SijIndicates that the word w is to be markedijIn evaluating text information xiWhich reflects the token word wijFor sample label yiDegree of influence of, while SijLarger indicates wijFor evaluation of text information xiThe more important.
And counting the importance parameters of each marked word in each sample in the evaluation text information D, and selecting the marked word with the maximum importance parameter in each piece of evaluation text information as the seed word of the evaluation text information.
205. And predicting corresponding marked words of the positions of the seed words based on the pre-training language representation model, acquiring a preset number of marked words to replace the seed words, and generating a confrontation sample to be measured for evaluating the text information.
In the embodiment of the present application, it is assumed that the text information x is evaluatediIs marked as wijWill evaluate the text information wijReplacement by [ MASK ]]And then the replaced evaluation text information xiCan be expressed as xi*＝[wi1,wi2,...,[MASK],...,wil]Then x is addedi*The words corresponding to the positions of the seed words are predicted by inputting the predicted words into a pre-trained language representation model, and the model structure is shown in the following figure 3. Will evaluate the text information xiOf the alternative text xi*Inputting the predicted seed word position corresponding mark word w into the pre-trained language representation modeli*After model prediction, the mark words with preset number at the position are obtained to replace the original evaluation text information xiTo generate new text
I.e. evaluating the text information xiThe amount of wait against the example.
206. And measuring a similarity parameter between the confrontation sample to be measured and the evaluation text information based on the semantic similarity model, selecting the confrontation sample to be measured corresponding to the maximum value of the similarity parameter as the confrontation sample of the evaluation text information, and generating an emotion analysis training extended sample set.
In order to further ensure semantic similarity between the countermeasure sample and the original evaluation text information, in the embodiment of the present application, a similarity parameter between the countermeasure sample to be measured and the evaluation text information is measured based on the semantic similarity model, the countermeasure sample to be measured corresponding to the maximum value of the similarity parameter is selected as the countermeasure sample of the evaluation text information, and an emotion analysis training extended sample set is generated.
207. And training a basic text emotion analysis model based on a training sample obtained by combining the emotion analysis training original sample set and the emotion analysis training extended sample set to obtain a text emotion analysis model.
In the embodiment of the application, the emotion analysis training extended sample set generated in step 206 and the training sample obtained by combining the emotion analysis training original sample set obtained in step 202 are trained on the basic text emotion analysis model obtained in step 203 to obtain the text emotion analysis model. Because the original training sample is expanded by one, the accuracy of the text emotion analysis model is greatly improved.
In another embodiment, for further explanation and limitation, the method of this embodiment further includes: if the emotion classification result is negative emotion, outputting an inquiry box, wherein the inquiry box is used for inquiring whether all objects under the management category of the target object are shielded; and if the object is shielded, matching the replacement object from the classification management database based on the user characteristic information, and pushing the replacement object, wherein the user characteristic information comprises the age, the sex and the borrowing record of the user.
Specifically, if the emotion classification result of the evaluation text information of the target object by the user is a negative emotion, it indicates that the user does not like or is not interested in the target object, and then the receiving of the information push associated with the target object is not desired with a high probability. And if the feedback for determining the shielding is received, executing the shielding operation, and inquiring a matched replacement object from the classification management database based on the user characteristic information to push so as to improve the experience of the user. Wherein the user characteristic information may include user age, user gender, borrowing records and the like. For example, the user a is a 23-year-old female user, and if the book borrowing proportion of the "urban language class" in the borrowing record is the highest, the books matched with the urban language class are pushed in the book classification management database.
It should be noted that, the basic information of the user characteristic information, such as the user age, the user gender, and the like, can be obtained when the user performs system registration, and the borrowing record needs to be updated in time after each login operation of the user, so as to ensure the timeliness of the information.
In another embodiment, for further explanation and limitation, the method of this embodiment further includes: and if no related object exists, outputting an attention ranking list, wherein the attention ranking list is used for representing the attention degrees of all the objects in the classification management database.
Specifically, if the associated object matched with the target object is found in the classification management database, in order to ensure the experience of the user, the attention ranking list may be output to the user. The interest ranking list is used for representing the interest degree of all the objects in the classification management database, for example, a popular book borrowing list, a book popular selling list, and the like.
The application provides an information pushing method based on a text countermeasure sample, which comprises the steps of firstly obtaining evaluation text information of a target object; secondly, classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training a training sample which is obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model; if the emotion classification result is positive emotion, extracting the management class and the keywords of the target object, and searching for the associated object with the similarity greater than a preset similarity threshold value with the keywords in a preset classification management database under the same management class with the target object; and finally, if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result. Compared with the prior art, the evaluation text information of the target object is classified by the text emotion analysis model obtained by training the training sample obtained by combining the expanded text information and the original text information, so that the accuracy of classification of the evaluation text information is greatly improved, and the effectiveness of information push according to the classification result is correspondingly improved.
Further, as an implementation of the method shown in fig. 1, an embodiment of the present application provides an information pushing apparatus based on a text countermeasure sample, as shown in fig. 4, the apparatus includes:
a first obtaining module 31, a classifying module 32, a searching module 33, and a first outputting module 34.
A first obtaining module 31, configured to obtain evaluation text information of a target object;
the classification module 32 is configured to perform classification processing on the evaluation text information based on a text emotion analysis model that has been trained by the model, so as to obtain an emotion classification result, where the text emotion analysis model is obtained by training a training sample obtained by combining extended text information and original text information, and the extended text information is used to represent a text countermeasure sample generated based on a mask language model;
the searching module 33 is configured to, if the emotion classification result is a positive emotion, extract a management category and a keyword of the target object, and search for an associated object having a similarity greater than a preset similarity threshold with the keyword in a preset classification management database under a management category that is the same as the target object;
a first output module 34, configured to, if the associated object exists, perform sorting based on the similarity value, and output the associated object according to a sorting result.
In a specific application scenario, before the classification module 32, the apparatus further includes:
the second acquisition module is used for acquiring the full-scale initial evaluation text information to obtain an emotion analysis training original sample set;
and the construction module is used for constructing an initial text emotion analysis model based on the convolutional neural network, and performing model training on the initial text emotion analysis model based on the emotion analysis training original sample set to obtain a basic text emotion analysis model.
In a specific application scenario, after the building module, the apparatus further includes:
the calculation module is used for calculating the importance parameters of the marked words in the evaluation text information based on the basic text sentiment analysis model, selecting the marked words corresponding to the maximum value of the importance parameters as seed words of the evaluation text information, and the evaluation text information is evaluation text information in the sentiment analysis training original sample set;
the prediction module is used for predicting corresponding marked words of the seed word positions based on a pre-training language representation model, acquiring a preset number of marked words to replace the seed words, and generating a confrontation sample to be measured of the evaluation text information;
and the measuring module is used for measuring the similarity parameter between the confrontation sample to be measured and the evaluation text information based on the semantic similarity model, selecting the confrontation sample to be measured corresponding to the maximum value of the similarity parameter as the confrontation sample of the evaluation text information, and generating an emotion analysis training extended sample set.
In a specific application scenario, after the metric module, the apparatus further includes:
and the training module is used for training the basic text emotion analysis model based on a training sample obtained by combining the emotion analysis training original sample set and the emotion analysis training extended sample set to obtain a text emotion analysis model.
In a specific application scenario, the second obtaining module specifically includes:
the acquisition unit is used for acquiring full-scale initial evaluation text information;
and the screening unit is used for screening the target character strings of the initial evaluation text information to obtain an emotion analysis training original sample set.
In a specific application scenario, the apparatus further includes:
a second output module, configured to output an inquiry box if the emotion classification result is a negative emotion, where the inquiry box is used to inquire whether to mask all objects in the management category of the target object;
and the matching module is used for matching a replacement object from the classification management database based on user characteristic information and pushing the replacement object if the replacement object is shielded, wherein the user characteristic information comprises the age, the sex and the borrowing record of the user.
In a specific application scenario, the apparatus further includes:
and the third output module is used for outputting a focus ranking list if the associated object does not exist, wherein the focus ranking list is used for representing the focused degrees of all the objects in the classification management database.
The application provides an information pushing device based on a text countermeasure sample, which comprises the steps of firstly obtaining evaluation text information of a target object; secondly, classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training a training sample which is obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model; if the emotion classification result is positive emotion, extracting the management class and the keywords of the target object, and searching for the associated object with the similarity greater than a preset similarity threshold value with the keywords in a preset classification management database under the same management class with the target object; and finally, if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result. Compared with the prior art, the evaluation text information of the target object is classified by the text emotion analysis model obtained by training the training sample obtained by combining the expanded text information and the original text information, so that the accuracy of classification of the evaluation text information is greatly improved, and the effectiveness of information push according to the classification result is correspondingly improved.
According to an embodiment of the present application, a storage medium is provided, where the storage medium stores at least one executable instruction, and the computer-executable instruction may execute the information pushing method based on the text countermeasure sample in any of the method embodiments described above.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the implementation scenarios of the present application.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application, and the specific embodiment of the present application does not limit a specific implementation of the computer device.
As shown in fig. 5, the computer apparatus may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein: the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.
The processor 402 is configured to execute the program 410, and may specifically execute relevant steps in the above-described information pushing method embodiment based on the text countermeasure sample.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a central processing unit CPU, or an application Specific Integrated circuit asic, or one or more Integrated circuits configured to implement embodiments of the present application. The computer device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may specifically be configured to cause the processor 402 to perform the following operations:
acquiring evaluation text information of a target object;
classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training samples which are obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model;
if the emotion classification result is positive emotion, extracting the management type and the key words of the target object, and searching for the associated object with the similarity larger than a preset similarity threshold value with the key words in a preset classification management database under the same management type with the target object;
and if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result.
The storage medium may further include an operating system and a network communication module. The operating system is a program for managing hardware and software resources of the entity device for pushing the information based on the text counterexample, and supports the running of an information processing program and other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the information processing entity device.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The method and system of the present application may be implemented in a number of ways. For example, the methods and systems of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present application may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present application. Thus, the present application also covers a recording medium storing a program for executing the method according to the present application.
It will be apparent to those skilled in the art that the modules or steps of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An information pushing method based on a text countermeasure sample is characterized by comprising the following steps:
acquiring evaluation text information of a target object;
classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training samples which are obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model;
if the emotion classification result is positive emotion, extracting the management type and the key words of the target object, and searching for the associated object with the similarity larger than a preset similarity threshold value with the key words in a preset classification management database under the same management type with the target object;
and if the associated objects exist, sorting based on the similarity values, and outputting the associated objects according to a sorting result.
2. The method of claim 1, wherein before the classification processing of the evaluation text information based on the text emotion analysis model after model training, the method further comprises:
acquiring full-scale initial evaluation text information to obtain an emotion analysis training original sample set;
an initial text emotion analysis model is established based on a convolutional neural network, model training is carried out on the initial text emotion analysis model based on an emotion analysis training original sample set, and a basic text emotion analysis model is obtained.
3. The method of claim 2, wherein after obtaining the base text emotion analysis model, the method further comprises:
calculating an importance parameter of a marked word in the evaluation text information based on the basic text sentiment analysis model, selecting the marked word corresponding to the maximum value of the importance parameter as a seed word of the evaluation text information, wherein the evaluation text information is evaluation text information in the sentiment analysis training original sample set;
predicting corresponding marked words of the seed word positions based on a pre-training language representation model, acquiring a preset number of marked words to replace the seed words, and generating a confrontation sample to be measured of the evaluation text information;
and measuring a similarity parameter between the confrontation sample to be measured and the evaluation text information based on a semantic similarity model, selecting the confrontation sample to be measured corresponding to the maximum value of the similarity parameter as the confrontation sample of the evaluation text information, and generating an emotion analysis training extended sample set.
4. The method of claim 3, wherein after generating the emotion analysis training extended sample set, the method further comprises:
and training the basic text emotion analysis model based on the training samples obtained by combining the emotion analysis training original sample set and the emotion analysis training extended sample set to obtain a text emotion analysis model.
5. The method according to claim 2, wherein the obtaining of the full-scale initial evaluation text information to obtain an emotion analysis training original sample set specifically comprises:
acquiring full-scale initial evaluation text information;
and screening target character strings for the initial evaluation text information to obtain an emotion analysis training original sample set.
6. The method of claim 1, further comprising:
if the emotion classification result is negative emotion, outputting an inquiry box, wherein the inquiry box is used for inquiring whether all objects under the management category of the target object are shielded;
and if the object is shielded, matching a replacement object from the classification management database based on user characteristic information, and pushing the replacement object, wherein the user characteristic information comprises the age, the sex and the borrowing record of the user.
7. The method of claim 1, further comprising:
and if the associated objects do not exist, outputting an attention ranking list, wherein the attention ranking list is used for representing the attention degrees of all the objects in the classification management database.
8. An information pushing apparatus based on a text countermeasure sample, comprising:
the first acquisition module is used for acquiring evaluation text information of a target object;
the classification module is used for classifying the evaluation text information based on a text emotion analysis model which is trained by a model, so as to obtain emotion classification results, wherein the text emotion analysis model is obtained by training samples which are obtained by combining expanded text information and original text information, and the expanded text information is used for representing a text countermeasure sample generated based on a mask language model;
the searching module is used for extracting the management category and the key word of the target object if the emotion classification result is positive emotion, and searching for the associated object of which the similarity with the key word is greater than a preset similarity threshold under the same management category as the target object in a preset classification management database;
and the first output module is used for sorting based on the similarity value if the associated object exists and outputting the associated object according to a sorting result.
9. A storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to execute operations corresponding to the information pushing method based on the text countermeasure sample according to any one of claims 1-7.
10. A computer device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the information pushing method based on the text countermeasure example in any one of claims 1-7.
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